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Open AccessArticle

Exploration of Generative Neural Networks for Police Facial Sketches

by Nerea Sádaba-Campo and Hilario Gómez-Moreno

Big Data Cogn. Comput. 2025, 9(2), 42; https://doi.org/10.3390/bdcc9020042 - 14 Feb 2025

Viewed by 2792

This article addresses the impact of generative artificial intelligence on the creation of composite sketches for police investigations. The automation of this task, traditionally performed through artistic methods or image composition, has become a challenge that can be tackled with generative neural networks. In this context, technologies such as Generative Adversarial Networks, Variational Autoencoders, and Diffusion Models are analyzed. The study also focuses on the use of advanced tools like DALL-E, Midjourney, and primarily Stable Diffusion, which enable the generation of highly detailed and realistic facial images from textual descriptions or sketches and allow for rapid and precise morphofacial modifications. Additionally, the study explores the capacity of these tools to interpret user-provided facial feature descriptions and adjust the generated results accordingly. The article concludes that these technologies have the potential to automate the composite sketch creation process. Therefore, their integration could not only expedite this process but also enhance its accuracy and utility in the identification of suspects or missing persons, representing a groundbreaking advancement in the field of criminal investigation. Full article

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19 pages, 5836 KB

Open AccessArticle

HE-CycleGAN: A Symmetric Network Based on High-Frequency Features and Edge Constraints Used to Convert Facial Sketches to Images

by Bin Li, Ruiqi Du, Jie Li and Yuekai Tang

Symmetry 2024, 16(8), 1015; https://doi.org/10.3390/sym16081015 - 8 Aug 2024

Cited by 1 | Viewed by 2607

Abstract

The task of converting facial sketch images to facial images aims to generate reasonable and clear facial images from a given facial sketch image. However, the facial images generated by existing methods are often blurry and suffer from edge overflow issues. In this study, we proposed HE-CycleGAN, a novel facial-image generation network with a symmetric architecture. The proposed HE-CycleGAN has two identical generators, two identical patch discriminators, and two identical edge discriminators. Therefore, HE-CycleGAN forms a symmetrical architecture. We added a newly designed high-frequency feature extractor (HFFE) to the generator of HE-CycleGAN. The HFFE can extract high-frequency detail features from the feature maps’ output, using the three convolutional modules at the front end of the generator, and feed them to the end of the generator to enrich the details of the generated face. To address the issue of facial edge overflow, we have designed a multi-scale wavelet edge discriminator (MSWED) to determine the rationality of facial edges and better constrain them. We trained and tested the proposed HE-CycleGAN on CUHK, XM2VTS, and AR datasets. The experimental results indicate that HE-CycleGAN can generate higher quality facial images than several state-of-the-art methods. Full article

(This article belongs to the Special Issue Symmetry with Optimization in Real-World Applications)

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12 pages, 1866 KB

Open AccessArticle

Backdoor Attack against Face Sketch Synthesis

by Shengchuan Zhang and Suhang Ye

Entropy 2023, 25(7), 974; https://doi.org/10.3390/e25070974 - 25 Jun 2023

Cited by 1 | Viewed by 2042

Abstract

Deep neural networks (DNNs) are easily exposed to backdoor threats when training with poisoned training samples. Models using backdoor attack have normal performance for benign samples, and possess poor performance for poisoned samples manipulated with pre-defined trigger patterns. Currently, research on backdoor attacks focuses on image classification and object detection. In this article, we investigated backdoor attacks in facial sketch synthesis, which can be beneficial for many applications, such as animation production and assisting police in searching for suspects. Specifically, we propose a simple yet effective poison-only backdoor attack suitable for generation tasks. We demonstrate that when the backdoor is integrated into the target model via our attack, it can mislead the model to synthesize unacceptable sketches of any photos stamped with the trigger patterns. Extensive experiments are executed on the benchmark datasets. Specifically, the light strokes devised by our backdoor attack strategy can significantly decrease the perceptual quality. However, the FSIM score of light strokes is 68.21% on the CUFS dataset and the FSIM scores of pseudo-sketches generated by FCN, cGAN, and MDAL are 69.35%, 71.53%, and 72.75%, respectively. There is no big difference, which proves the effectiveness of the proposed backdoor attack method. Full article

(This article belongs to the Special Issue Trustworthy AI: Information Theoretic Perspectives)

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18 pages, 5154 KB

Open AccessArticle

Conditional Generative Adversarial Networks with Total Variation and Color Correction for Generating Indonesian Face Photo from Sketch

by Mia Rizkinia, Nathaniel Faustine and Masahiro Okuda

Appl. Sci. 2022, 12(19), 10006; https://doi.org/10.3390/app121910006 - 5 Oct 2022

Cited by 8 | Viewed by 4218

Abstract

Historically, hand-drawn face sketches have been commonly used by Indonesia’s police force, especially to quickly describe a person’s facial features in searching for fugitives based on eyewitness testimony. Several studies have been performed, aiming to increase the effectiveness of the method, such as comparing the facial sketch with the all-points bulletin (DPO in Indonesian terminology) or generating a facial composite. However, making facial composites using an application takes quite a long time. Moreover, when these composites are directly compared to the DPO, the accuracy is insufficient, and thus, the technique requires further development. This study applies a conditional generative adversarial network (cGAN) to convert a face sketch image into a color face photo with an additional Total Variation (TV) term in the loss function to improve the visual quality of the resulting image. Furthermore, we apply a color correction to adjust the resulting skin tone similar to that of the ground truth. The face image dataset was collected from various sources matching Indonesian skin tone and facial features. We aim to provide a method for Indonesian face sketch-to-photo generation to visualize the facial features more accurately than the conventional method. This approach produces visually realistic photos from face sketches, as well as true skin tones. Full article

(This article belongs to the Special Issue Recent Advances in Deep Learning for Image Analysis)

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15 pages, 4577 KB

Open AccessArticle

Multi-Level Cycle-Consistent Adversarial Networks with Attention Mechanism for Face Sketch-Photo Synthesis

by Danping Ren, Jiajun Yang and Zhongcheng Wei

Sensors 2022, 22(18), 6725; https://doi.org/10.3390/s22186725 - 6 Sep 2022

Cited by 2 | Viewed by 2480

Abstract

The synthesis between face sketches and face photos has important application values in law enforcement and digital entertainment. In cases of a lack of paired sketch-photo data, this paper proposes an unsupervised model to solve the problems of missing key facial details and a lack of realism in the synthesized images of existing methods. The model is built on the CycleGAN architecture. To retain more semantic information in the target domain, a multi-scale feature extraction module is inserted before the generator. In addition, the convolutional block attention module is introduced into the generator to enhance the ability of the model to extract important feature information. Via CBAM, the model improves the quality of the converted image and reduces the artifacts caused by image background interference. Next, in order to preserve more identity information in the generated photo, this paper constructs the multi-level cycle consistency loss function. Qualitative experiments on CUFS and CUFSF public datasets show that the facial details and edge structures synthesized by our model are clearer and more realistic. Meanwhile the performance indexes of structural similarity and peak signal-to-noise ratio in quantitative experiments are also significantly improved compared with other methods. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Sensing and Image Processing)

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